This paper investigates whether source trustworthiness shapes Turkish evidential morphology and whether large language models (LLMs) track this sensitivity. We study the past-domain contrast between -DI and -mIs in controlled cloze contexts where the information source is overtly external, while only its perceived reliability is manipulated (High-Trust vs. Low-Trust). In a human production experiment, native speakers of Turkish show a robust trust effect: High-Trust contexts yield relatively more -DI, whereas Low-Trust contexts yield relatively more -mIs, with the pattern remaining stable across sensitivity analyses. We then evaluate 10 LLMs in three prompting paradigms (open gap-fill, explicit past-tense gap-fill, and forced-choice A/B selection). LLM behavior is highly model- and prompt-dependent: some models show weak or local trust-consistent shifts, but effects are generally unstable, often reversed, and frequently overshadowed by output-compliance problems and strong base-rate suffix preferences. The results provide new evidence for a trust-/commitment-based account of Turkish evidentiality and reveal a clear human-LLM gap in source-sensitive evidential reasoning.

翻译：本文研究信息来源可信度是否塑造土耳其语的证据形态，以及大型语言模型（LLM）能否追踪这种敏感性。我们在受控完形填空语境中考察-dI与-mIs的过去时态对立——信息源被明确标记为外部来源，仅其感知可信度受到操纵（高信任度 vs 低信任度）。人类产出实验显示，土耳其语母语者表现出稳健的信任效应：高信任度语境更倾向使用-dI，低信任度语境更倾向使用-mIs，该模式在敏感性分析中保持稳定。随后我们在三种提示范式（开放式填空、显式过去时填空、强制选择A/B对比）中评估了10个LLM。LLM的行为高度依赖模型和提示设置：部分模型显示出微弱或局部的信任一致性偏移，但整体效应不稳定，常出现反转，且普遍受输出合规性问题与强基线后缀偏好的影响。研究结果为基于信任/承诺的土耳其语证据性解释提供了新证据，并揭示了人类与LLM在源敏感推理能力上的显著差距。